Method of evaluating a part

ABSTRACT

A method of evaluating a part is characterized by obtaining data representing a distress rank model (DRM) and a cumulative damage model (CDM) for the part. Based on the data, the method ascertains a DRM value for the part and a CDM value for the part. The method determines whether the DRM value is at or above a predetermined DRM threshold and whether a CDM value is at or above a predetermined CDM threshold. If either the DRM value or the CDM value is at or above at least one respective threshold, an action related to the part is generated.

BACKGROUND OF THE INVENTION

Diagnostics and prognostics, as applied to the operation of complexsystems such as aircraft, aircraft engines, medical equipment, powerplants etc., provide data and estimates that relate to the fitness forservice and remaining life of the individual components of the system.Dependable evaluation of individual components of complex systems allowssystem planners to better operate and maintain these complex systems.For example, with respect to aircraft, early detection of hardwaredistress is vital to preventing in-flight shutdowns, unplanned engineremovals and/or secondary hardware damage. With earlier detection,system planners may more readily schedule maintenance and replacehardware without the need for a full system overhaul.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, a method of evaluating a part is characterized byobtaining data representing a distress rank model (DRM) for the part;ascertaining a DRM value for the part; obtaining data representing acumulative damage model (CDM) for the part; ascertaining a CDM value forthe part; determining whether the DRM value is at or above at least onepredetermined DRM threshold; and determining whether a CDM value is ator above at least one predetermined CDM threshold. If either the DRMvalue or the CDM value is at or above at least one respective threshold,an action related to the part generated.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a perspective view of an aircraft during a maintenanceprocedure during which diagnostic data may be gathered.

FIG. 2 is a cross-sectional side view of a typical gas turbine enginefrom which diagnostic data may be gathered.

FIG. 3 is a scatter diagram depicting a distress rank model versus adomain for a set of damaged and non-damaged parts.

FIG. 4 is a scatter diagram depicting a cumulative damage model versus adomain for a set of parts.

FIG. 5 is a scatter diagram depicting a cumulative damage model versus adistress rank model for a set of damaged and non-damaged parts.

FIG. 6 is a flow chart depicting a method of evaluating an aircraftpart.

DETAILED DESCRIPTION OF THE INVENTION

In the background and the following description, for the purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the technology described herein. It will beevident to one skilled in the art, however, that the exemplaryembodiments may be practiced without these specific details. In otherinstances, structures and device are shown in diagram form in order tofacilitate description of the exemplary embodiments.

The exemplary embodiments are described with reference to the drawings.These drawings illustrate certain details of specific embodiments thatimplement a module, method, or computer program product describedherein. However, the drawings should not be construed as imposing anylimitations that may be present in the drawings.

FIG. 1 schematically depicts one embodiment of an exemplary aircraft 10during a maintenance operation transmitting data, ultimately, to a dataoperations center 50 that will execute embodiments of the diagnostic andprognostic method presented herein. The aircraft 10 includes one or morecomplex systems that include multitudinous parts therein, the complexsystems relating to various aspects of the aircraft. The aircraftincludes one or more propulsion engines 12, a fuselage 14 with a cockpit16 positioned in the fuselage 14, and the one or more propulsion engines12 coupled to the fuselage 14 directly or, as shown, by way of wingassemblies 18 extending outward from the fuselage 14. While a commercialaircraft has been illustrated, it is contemplated that embodiments ofthe invention may be used in any type of complex system, including, forexample, power plants, ships, trains, buildings, space craft, andaircraft different than the present embodiment, including fixed-wing,rotating-wing, rocket, personal aircraft, etc.

A plurality of aircraft subsystems 20 that enable proper operation ofthe aircraft 10 may be included in the aircraft 10 as well as one ormore computers or controllers 22, which may be operably coupled to theplurality of aircraft subsystems 20 to control their operation. Whileonly a single controller 22 has been illustrated, it is contemplatedthat any number of controllers 22 may be included in the aircraft 10. Insuch an instance, the controller 22 may also be connected with othercontrollers of the aircraft 10. The controller 22 may include or beassociated with any suitable number of individual microprocessors, powersupplies, storage devices, interface cards, auto flight systems, flightmanagement computers, and other standard components. In addition tocomponents for the proper operation of the aircraft, an aircraftsubsystem 20 may include sensor components for observing, gathering andtransmitting data related to the operational life of the parts of thesystem. The data may then be transmitted to one or more controllers 22.

The controller 22, possibly including a health management unit (notshown), may be communicably coupled to one or more communication linksto transfer data to and from the aircraft 10. It is contemplated thatthe communication links may be wireless communication links and may beany variety of communication mechanism capable of wirelessly linkingwith other systems and devices and may include, but is not limited to,packet radio, satellite uplink and/or downlink, Wireless Fidelity(WiFi), WiMax, Bluetooth, ZigBee, 3G wireless signal, code divisionmultiple access (CDMA) wireless signal, global system for mobilecommunication (GSM), 4G wireless signal, long term evolution (LTE)signal, Ethernet, or any combinations thereof. It will also beunderstood that the particular type or mode of wireless communication isnot critical to embodiments of the invention, and later-developedwireless networks are certainly contemplated as within the scope ofembodiments of the invention. Further, the communication links mayinclude one or more radios including voice, ACARS-analog, ACARS-digital,SATCOM, cellular, etc. The communication links may allow forcommunication with maintenance personnel via, for example a maintenancevehicle 40, ground controllers or data operations center 50 at aground-based station or with non-ground stations such as satellite (notshown).

Further, while data communicated to the data operations center 50 via amaintenance vehicle 40 has been illustrated, it will be understood thatthe aircraft 10 may communicate directly with the data operations center50 utilizing the communication links. At the data operations center 50,a computing system (termed “a processor”) processes the data transmittedby the aircraft 10 over the communications link to evaluate an aircraftpart and direct further maintenance activities to either identify orrectify the identified issues. The processor may require relativelylarge amounts of computing power and time and may be performed during amaintenance operation or across multiple flights and maintenanceoperations.

It will be understood that details of environments that may implementembodiments of the invention are set forth in order to provide athorough understanding of the technology described herein. It will beevident to one skilled in the art, however, that the exemplaryembodiments may be practiced without these specific details. Thedrawings illustrate certain details of specific embodiments thatimplement a module or method, or computer program product describedherein. However, the drawings should not be construed as imposing anylimitations that may be present in the drawings. The method and computerprogram product may be provided on any machine-readable media foraccomplishing their operations. The embodiments may be implemented usingan existing computer processor, or by a special purpose computerprocessor incorporated for this or another purpose, or by a hardwiredsystem.

As noted above, embodiments described herein may include a computerprogram product comprising machine-readable media for carrying or havingmachine-executable instructions or data structures stored thereon. Suchmachine-readable media may be any available media, which may be accessedby a general purpose or special purpose computer or other machine with aprocessor. By way of example, such machine-readable media can compriseRAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other medium thatcan be used to carry or store desired program code in the form ofmachine-executable instructions or data structures and that can beaccessed by a general purpose or special purpose computer or othermachine with a processor. When information is transferred or providedover a network or another communication connection (either hardwired,wireless, or a combination of hardwired or wireless) to a machine, themachine properly views the connection as a machine-readable medium.Thus, any such a connection is properly termed a machine-readablemedium. Combinations of the above are also included within the scope ofmachine-readable media. Machine-executable instructions comprise, forexample, instructions and data, which cause a general-purpose computer,special purpose computer, or special purpose processing machines toperform a certain function or group of functions.

Embodiments will be described in the general context of method stepsthat may be implemented in one embodiment by a program product includingmachine-executable instructions, such as program codes, for example, inthe form of program modules executed by machines in networkedenvironments. Generally, program modules include routines, programs,objects, components, data structures, etc. that have the technicaleffect of performing particular tasks or implement particular abstractdata types. Machine-executable instructions, associated data structures,and program modules represent examples of program codes for executingsteps of the method disclosed herein. The particular sequence of suchexecutable instructions or associated data structures represent examplesof corresponding acts for implementing the functions described in suchsteps.

Embodiments may be practiced in a networked environment using logicalconnections to one or more remote computers having processors. Logicalconnections may include a local area network (LAN) and a wide areanetwork (WAN) that are presented herein by way of example and notlimitation. Such networking environments are commonplace in office-wideor enterprise-wide computer networks, intranets and the internet and mayuse a wide variety of different communication protocols. Those skilledin the art will appreciate that such network computing environments willtypically encompass many types of computer system configurations,including personal computers, hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, and the like.

Embodiments may also be practiced in distributed computing environmentswhere tasks are performed by local and remote processing devices thatare linked (either by hardwired links, wireless links, or by acombination of hardwired or wireless links) through a communicationnetwork. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

At the data operations center 50, a processor may obtain and ascertaindata to evaluate an aircraft part, collection of parts, a system orsubsystem and direct further maintenance activities to either identifyor rectify the identified issues. As an example of a complex system thatincludes multitudinous parts subject to observation, data gathering,diagnostics and prognostication, consider a gas turbine engine 12 on anaircraft 10. Referring now to FIG. 2, a schematic cross-sectionaldiagram of a gas turbine engine 12 for an aircraft is shown. The engine12 includes, in downstream serial flow relationship, a fan section 112including a fan 114, a booster or low pressure (LP) compressor 116, ahigh pressure (HP) compressor 118, a combustion section 120, a HPturbine 122, and a LP turbine 124. A HP shaft or spool 126 drivinglyconnects HP turbine 122 to HP compressor 118 and a LP shaft or spool 128drivingly connects LP turbine 124 to LP compressor 116 and fan 114. HPturbine 122 includes a HP turbine rotor 130 having turbine blades 132mounted at a periphery of rotor 130. Blades 132 extend radiallyoutwardly from blade platforms 134 to radially outer blade tips 136.

The engine 12 is shown mounted to the pylon 138, at a pylon bracket 140,by both aft and fore engine mounts 142. The pylon 138, as illustrated,further secures to the aircraft wing 144, but may be secured atalternate positions of the aircraft, such as the fuselage.

The engine 12 further includes an outer cowl 146 and an inner cowl 148,each having smooth surfaces to reduce the drag of air passing inside oroutside the engine 12 in flight. The outer cowl 146 encircles at least aportion of the inner cowl 148 and the engine 12. The pylon 138 furthercomprises bifurcation walls 150, partially extending from the pylon 138toward the inner cowl 148, defining a gap 152.

Also illustrated are a multitude of connector lines 154, such ashydraulic lines, electrical lines and bypass air lines, extending fromthe engine 12 through the bifurcation walls 150 into the pylon 138.These lines 154 couple the engine 12 to subsystems necessary foroperation, such as fuel pumps and flight control computers.

Sensors proximate or coupled to any of the aircraft parts may observeand generate data related to one or more sensed physical parameters ofthe parts of the system. Example physical parameters may derive frommeasurements of properties such as, but not limited to, pressure,temperature, strain, etc. The data may include measurements correlatedto the amount of time or number of cycles that a given aircraft part issubject to various levels of pressure, temperature, strain etc. Duringmaintenance operations, observations may result from visual inspectionof an aircraft part. The sensed or observed data may result fromobservation via visual inspection with or without the aid ofelectro-optical equipment such as a boresight system or the aid ofradiometric or spectrographic systems. Pre-processing of observationdata may include processing observation data with image enhancement orpredictive algorithms. Modeling of the data with processing-intensiveroutines such as provided by finite element analysis may further enhancethe observational data.

Data acquired are not limited to sensors proximate or coupled to themonitored equipment, but can include environmental data such as weatherdata, geographical location, satellite images, and any other data orinformation or knowledge that will improve accuracy and precision ofprognostic and diagnostic process.

In addition to the above, data acquired can be inferred data or datathat is not measured directly but can be inferred using one or moremeasured data either in combination of understanding the physics of thepart or the system or not. In one non-limiting example, sometemperatures inside an engine cannot be directly measured but can beinferred by knowing engine cycle physics and other directly measuredparameters that may include environmental parameters or engine sensorparameters.

As described above, the processor at the data operations center 50obtains data relating to the observed state, condition or operationalenvironment of an aircraft part, which data may be plotted as shown forexample in a scatter diagram 200 in FIG. 3. Accordingly, the processorobtains data representing a distress rank model (DRM) 202 for theaircraft part. The DRM 202 ranks the relative observed hardware distresslevels and then uses regression analysis of multiple aircraft partsensor data to determine an optimum transfer function to ascertain a DRMvalue that quantifies the hardware distress for the aircraft part. Forexample, for a domain described as x, a sample linear regression modelfor the DRM 202 may be of the form: y_(DRM)=β₀+β₁x. The model is fit tothe data using methods common to regression analysis, for example, byordinary least squares, etc. A transfer function formed from the DRM202, among other things, may forecast how the level of distress to anaircraft part will progress along the domain (e.g. cycles).

FIG. 3 depicts an exemplary DRM 202 versus a domain 204 for a setrepresenting damaged and non-damaged parts. Multiple data pointsdepicting DRM values for aircraft parts are shown, plotted on they-axis. Both non-damaged and damaged parts are shown where the damagedparts indicate an aircraft part that is shown to be damaged through avisual inspection of the part that confirms that the part is damaged toa point where it needs to be replaced. The domain 204 on the x-axisdepicts a characteristic such as time duration an aircraft engine isrunning or number of cycles (i.e. the number of times an engine is takenfrom start to high power to shutdown). As shown in the scatter diagram,a threshold 210 may be predetermined such that an aircraft part with aDRM value above the predetermined threshold may be indicative of adamaged part. As seen in FIG. 3, the threshold does not perfectlydelineate between actually damaged and non-damaged parts. Instead, somedamaged parts have a DRM value below the threshold and constitute misseddetections and some non-damaged parts have a DRM value above thethreshold and constitute false positives.

The processor at the data operations center 50 also obtains datarelating to a physical phenomenon representing an aircraft part such asmay be derived from sensors, which data may be plotted as shown forexample in a scatter diagram 300 in FIG. 4. Accordingly, the processorobtains data representing a cumulative damage model (CDM) 302 for theaircraft part. The CDM 302 determines the physical phenomenon drivingthe distress of an aircraft part and then evaluates the data to quantifythe relative hardware distress of the aircraft part. To arrive at avalue of the relative hardware distress of the aircraft part, the CDM302 uses a physics-based model of an aircraft part's lifecycle. In thisway, the CDM 302 accumulates over the domain. For example, the CDM 302may indicate the total time an aircraft part has been at or above acritical temperature as a function of engine cycles.

FIG. 4 depicts an exemplary CDM 302 versus a domain 304 for a set ofparts. The CDM 302 determines the physical phenomenon driving thedistress and describes the relative hardware distress. Multiple datapoints depicting CDM values for aircraft parts are shown, plotted on they-axis. The domain 304 on the x-axis depicts a characteristic such astime duration an aircraft engine is running or cycles over which the CDM302 accumulates. As shown in the scatter diagram, a threshold 310 may bepredetermined such that an aircraft part with a CDM value above thepredetermined threshold may be indicative of a damaged part.

According to embodiments of the present invention, the processor at thedata operations center 50 executes a method to combine the two separate,but complimentary, analytical algorithms of the DRM 202 and the CDM 302to independently quantify the relative distress and remaining life ofthe various gas turbine hardware parts. The DRM 202 is a diagnosticfunction that detects the actual, observed distress on the aircraftpart. The CDM 302 is a prognostic function that independently calculatescomponent life consumption based on actual engine operation. Incombination, the detection capability of a hardware part distress isfurther enhanced. FIG. 5 is a scatter diagram 400 depicting the CDM 302versus the DRM 202 for a set of damaged and non-damaged parts. Both theDRM threshold 210 and the CDM threshold 310 are shown. Additionalthresholds, shown as a second DRM threshold 426 and a second CDMthreshold 424 in FIG. 5, may also be predetermined. It is contemplatedthat each model may include a plurality of stepped thresholds.

Based on the ascertained values for the DRM and the CDM, an aircraftpart is located in a zone defined by the model values and its relationto the thresholds. Based on a zone for the part, the processor maygenerate an action related to the aircraft part and its CDM and DRMvalues. For example, the most critical threshold zone 410 is located inthe upper right of the diagram in FIG. 5 where the values for the givenpart may exceed both DRM and CDM thresholds 210 and 310, respectively.When the processor determines an aircraft part is located in thecritical threshold zone 410 because both the DRM and CDM exceed theirrespective thresholds 210 and 310, the processor may generate an urgentnotification that requires inspection, repair or replacement of theaircraft part. If only the DRM value meets or exceeds the threshold 210(and the aircraft part's model values are located in the threshold zone414), or if only the CDM threshold meets or exceeds the threshold 310(and the aircraft part's model values are located in the threshold zone412), the processor may generate a normal notification for inspection,repair or replacement of the aircraft part. Additional threshold zones416, 420, 418 are indicative of aircraft parts that the processordetermines to be within a certain limit of requiring inspection, repairor replacement. In the case where the CDM and DRM are based on a domainof cycles, these zones are indicative of an aircraft part within apredetermined number of cycles of issuance of a notification forinspection, repair or replacement. Similarly, where the processordetermines that the aircraft part does not exceed any thresholds (e.g.threshold zone 422), the processor may still estimate the number ofcycles until a maintenance action and issue a notification with theestimate.

FIG. 6 is a flow chart depicting a method 500 of evaluating an aircraftpart according to an embodiment. The processor integrates the two modelstogether in a process where CDM and DRM thresholds are set to maximizethe hardware distress detection capability of the method 500. At step510, the processor obtains data representing a DRM and ascertains a DRMvalue for an aircraft part. Each aircraft part is uniquely identifiedsuch as by part number or serial number. At step 512, the processorobtains data representing the CDM and ascertains a CDM value for theaircraft part. The processor at step 514 determines if the DRM value orthe CDM value for the aircraft part is within or approaching either theDRM or the CDM threshold. If neither the DRM value nor the CDM value forthe aircraft part is within the respective threshold, then theprocessor, at step 520, estimates the time as expressed for example asengine hours or cycles where the operator of the engine may have to takea predetermined action, for example inspection, maintenance removal orreplacement. Otherwise, at step 522, the processor generates and storesestimates relating to when an action is likely to be required for theaircraft part in a database. Subsequent to either steps 520 or 522, theprocessor may transmit remaining time or cycles information related tothe aircraft part to airline planners or maintenance personnel at step530.

If the processor, at step 516, determines that the aircraft part's DRMvalue or CDM value (but not both) exceeds a respective threshold, thenthe processor may issue a normal urgency advisory at step 524. If theprocessor determines at step 518 that the aircraft part's DRM value andCDM value each exceeds its respective threshold, then the processor mayissue an urgent advisory at step 526.

Subsequent to any of steps 524, 526 or 530, maintenance personnel mayreact to an advisory or to remaining time or cycles information andperform a recommended action of the aircraft part at step 528. Any newinspection results are stored by aircraft part number in step 532. Theprocessor receives the inspection information and uses the informationas a feedback mechanism at step 534. Based on generating and monitoringthe feedback which includes the DRM value, the CDM value and the resultsof the visual inspection determination of the amount of damage of theaircraft part, the processor may improve the models (i.e. the DRM andCDM) as well as the thresholds used to generate actions.

The above-described method may include additional or alternativenon-limiting steps. In one non-limiting example of alternative oradditional steps to the method, the processor includes steps to executeone or more voting algorithms to integrate the results of the CDM andDRM into a specific action with low probability of false positives ornegatives. In another non-limiting example, includes the developments,generation or implementation of a fused CDM/DRM model. In such a fusedmodel, the processor fuses the physics knowledge underpinning the CDMand the empirical data findings underpinning DRM together to create asingle fused model.

The above-described embodiments evaluate aircraft parts by diagnosingand prognosticating the condition of said parts based on obtained data.The resulting detection capability is then used to enhance enginediagnostics, quantify hardware distress and potentially improve enginetime-on-wing (TOW).

Technical effects of the above-described embodiments include fasterdecision support as the method reduces the disruption of unplannedengine removals, prevents the added expense of secondary hardware damageand improves the TOW between engine overhauls. In addition, embodimentsof the method reduce the airline inspection burden for engines where thedistress levels are below the predetermined distress threshold.

The method provides airline customers and service providers with keyknowledge to better manage and maintain their operating fleet. Reducingunplanned engine removals and increasing time between engine overhaulsimproves engine availability and reduces operating costs and allows forimproved long-term prediction of future distress and overhauls therebyreducing the risk on multi-year service plans.

This written description uses examples to disclose the invention andalso to enable any person skilled in the art to practice the invention,including making and using any devices or systems and performing anyincorporated methods. The patentable scope of the invention is definedby the claims, and may include other examples that occur to thoseskilled in the art. Such other examples are intended to be within thescope of the claims if they have structural elements that do not differfrom the literal language of the claims, or if they include equivalentstructural elements with insubstantial differences from the literallanguages of the claims.

What is claimed is:
 1. A method of evaluating a part characterized by:obtaining data representing a distress rank model (DRM) for the part;ascertaining a DRM value for the part; obtaining data representing acumulative damage model (CDM) for the part; ascertaining a CDM value forthe part; determining whether the DRM value is at or above at least onepredetermined DRM threshold; determining whether a CDM value is at orabove at least one predetermined CDM threshold; and if either the DRMvalue or the CDM value is at or above the at least one respectivethreshold, then generating an action related to the part.
 2. The methodof claim 1 wherein the generated action includes at least one of anotification, an inspection, a reminder, a replacement, or a repair. 3.The method of claim 2 wherein if both the DRM value and the CDM valueare at or above the at least one respective threshold, then raising theurgency of the generated action.
 4. The method of claim 1 wherein ifboth the DRM value and the CDM value are at or above the at least onerespective threshold, then requiring repair or replacement of the part.5. The method of claim 1 wherein ascertaining the DRM value is based atleast in part on an observation of the part.
 6. The method of claim 5wherein the observation includes at least one of visual inspection,photographic, radiometric, or other observation of the part.
 7. Themethod of claim 5 wherein the observation includes at least oneenvironmental measurement.
 8. The method of claim 5 wherein theobservation includes at least one unmeasured but estimated measurement.9. The method of claim 1 wherein ascertaining the CDM value is based atleast in part on measuring of a physical parameter or finite elementanalysis or predictive algorithms associated with the part.
 10. Themethod of claim 9 wherein the physical parameter includes at least oneof temperature, pressure, time, cycles, weather and environmental data.11. The method of claim 1 further comprising generating and monitoringfeedback from which the DRM and the CDM can be improved.
 12. The methodof claim 1 further comprising predicting when the part will requirerepair or replacement based on both the DRM and CDM values.
 13. Themethod of claim 1 wherein the DRM includes a regression model.